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Liver-shape analysis and quantification is still an open research subject. Quantitative assessment of the liver is of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Liver-shape classification is of clinical importance for corresponding intra-subject and inter-subject studies. In this research, we propose a novel technique for the liver-shape classification based on Spherical Harmonics (SH) coefficients. The proposed liver-shape classification algorithm consists of the following steps: (a) Preprocessing, including mesh generation and simplification, point-set matching, and surface to template alignment; (b) Liver-shape parameterization, including surface normalization, SH expansion followed by parameter space registration; (c) Feature selection and classification, including frequency based feature selection, feature space reduction by Principal Component Analysis (PCA), and classification. The above multi-step approach is novel in the sense that registration and feature selection for liver-shape classification is proposed and implemented and validated for the normal and diseases liver in the SH domain. Various groups of SH features after applying conventional PCA and/or ordered by p-value PCA are employed in two classifiers including Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) in the presence of 101 liver data sets. Results show that the proposed specific features combined with classifiers outperform existing liver-shape classification techniques that employ liver surface information in the spatial domain. In the available data sets, the proposed method can successful classify normal and diseased livers with a correct classification rate of above 90 %. The performed result in average is higher than conventional liver-shape classification method. Several standard metrics such as Leave-one-out cross-validation and Receiver Operating Characteristic (ROC) analysis are employed in the experiments and confirm the effectiveness of the proposed liver-shape classification with respect to conventional techniques.

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Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to external forces or body position changes during scanning procedures. We developed and tested a population-based model to represent the shape of liver.
Upper abdominal CT-scan input images are represented by a conventional shape model. The shape parameters of individual livers extracted from the CT scans are employed to classify them into different populations. Corresponding to each population, an SSM model is built. The liver surface parameter space is divided into several subspaces which are more compact than the original space. The proposed model was tested using 29 CT-scan liver image data sets. The method was evaluated by model compactness, reconstruction error, generality and specificity measures.
The proposed model is implemented and tested using CT scans that included liver shapes with large shape variations. The method was compared with conventional and recently developed shape modeling methods. The accuracy of the proposed model was nearly twice that achieved with the conventional model. The proposed population-based model was more general compared with the conventional model. The mean reconstruction error of the proposed model was 0.029 mm while that of the conventional model was 0.052 mm.
A population-based model to represent the shape of liver was developed and tested with favorable results. Using this approach, the liver shapes from CT scans were modeled by a more compact, more general, and more accurate model.

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PURPOSE: To quantify prostatic swelling and shift of intra-prostatic points during HIFU using real-time ultrasound. MATERIALS AND METHODS: The institutional review board approved this retrospective study. Forty-four patients with clinically localized prostate cancer underwent whole gland HIFU. Three sessions of HIFU were required to cover the entire prostate; anterior zone (1st-session), middle zone (2nd-session), and posterior zone of the prostate (3rd-session). Computer-assisted 3D reconstructions based on 3mm step-sectional images of intra-operative transrectal ultrasound were compared before and after each session. RESULTS: Most of the prostate swelling and shift of intra-prostatic points occurred during the 1st-session, with median percent increase in volume of 18% for the transition zone (TZ), 9% for the peripheral zone (PZ), and 13% for the entire prostate. The percent increase in TZ volume (p<0.001), PZ volume (p=0.001), and the entire prostate volume (p=0.001) were statistically dependent on the volume of each measured pre-operatively. Median 3D intra-prostatic shift was 3.7 mm (range: 0.9-13 mm) in the TZ, and 5.5 mm (range: 0.2-14 mm) in the PZ. Significant negative linear correlation was found between the pre-operative presumed circle area ratio (PCAR) and percent increase in prostate volume (p=0.001) and shift (p=0.01) during HIFU. CONCLUSIONS: Significant prostatic swelling and shift of the prostate were quantified during HIFU. Smaller prostates and smaller PCAR were associated with larger prostatic swelling and intra-prostatic shifts. Real-time intra-operative adjustment of the treatment plan has an impact on achieving precise targeting during HIFU especially in the prostate with smaller volume and/or smaller PCAR.

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We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace representing inter-subject shape variability. The first PCA is applied to the datasets of healthy organ shapes to define the subspace of normal variability. Then, the datasets of diseased shapes are projected onto the orthogonal complement (OC) of the sub-space of normal variability, and the second PCA is applied to the projected datasets to derive the subspace representing the disease-specific variability. To calculate the OC of an n-dimensional subspace, a novel closed-form formulation is developed. Experiments were performed to show that the support vector machine classification in the OC subspace better discriminated healthy and diseased liver shapes using 99 CT data. The effects of the number of training data and the difference in segmentation methods on the classification accuracy were evaluated to clarify the characteristics of the proposed method.

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Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

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The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.

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Automated segmentation of multiple organs in CT data of the upper abdomen is addressed. In order to explicitly incorporate the spatial interrelations among organs, we propose a method for finding and representing the interrelations based on canonical correlation analysis. Furthermore, methods are developed for constructing and utilizing the statistical atlas in which inter-organ constraints are explicitly incorporated to improve accuracy of multi-organ segmentation. The proposed methods were tested to perform segmentation of eight abdominal organs (liver, spleen, kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from various imaging conditions of CT datasets. 87 datasets acquired at two institutions were used for the validation. Significant accuracy improvement was observed for several organs in comparison with the conventional method.

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Atlas-based methods for automated preoperative planning of the femoral stem implant in total hip arthroplasty are described. Statistical atlases are constructed from a number of past preoperative plans prepared by experienced surgeons in order to represent the surgeon's expertise of the planning. Two types of atlases are considered. One is a statistical distance map atlas, which represents surgeon's preference of the contact pattern between the femoral canal (host bone) and stem (implant) surfaces. The other is an optimal reference plan, which is selected as the best representative plan expected to minimize the deviation from the surgeon's preferred contact pattern. These atlases are fitted to the patient data to automatically generate the preoperative plan of the femoral stem. In this paper, we formulate a general framework of atlas-based implant planning, and then describe the methods for construction and utilization of the two proposed atlases. In the experiments, we used 40 cases to evaluate the proposed methods and compare them with previous methods by defining the errors as differences between automated and surgeon's plans. By using the proposed methods, the positional and orientation errors were significantly reduced compared with the previous methods and the size error was superior to inter-surgeon variability in size selection using 2D templates on an X-ray image reported in previous work.

[Show abstract][Hide abstract]ABSTRACT:
The automated segmentation of multiple organs in CT data of the upper abdomen is addressed. In order to explicitly incorporate the spatial interrelations among organs, we propose a method for finding and representing the interrelations based on canonical correlation analysis. Furthermore, methods are developed for constructing and utilizing the statistical atlas in which inter-organ constraints are explicitly incorporated to improve accuracy of multi-organ segmentation. The proposed methods were tested to perform segmentation of seven abdominal organs (liver, spleen, kidneys, pancreas, gallbladder and inferior vena cava) from contrast-enhanced CT datasets and was compared to a previous approach. 28 datasets acquired at two institutions were used for the validation. Significant accuracy improvement was observed for the segmentation of pancreas and gallbladder while there was no accuracy reduction for any organ.

Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications; 09/2011

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Extraction and enhancement of tubular structures are important in image processing applications, especially in the analysis of liver CT scans where delineation of vascular structures is needed for surgical planning. Portal vein cross-sections have circular or elliptical shapes, so an algorithm must accommodate both. A vessel segmentation method based on medial-axis points was developed and tested on portal veins in CT images.
A medial-axis enhancement filter was developed. Consider a line passing through a point inside a tube and intersecting the edges of the tube. If the point is located on the medial axis, the distance of the point in the direction of the line to the edges of the tube will be equal. This feature was employed in a multi-scale framework to identify liver vessels. Dynamic thresholding was used to reduce noise sensitivity. The isotropic coefficient introduced by Pock et al. was used to reduce the response of the filter for asymmetric cross-sections.
Quantitative and qualitative evaluation of the proposed method were performed using both 2D/3D and synthetic/clinical datasets. Compared to other methods for medial-axis enhancement, our method produces better results in low-resolution CT images. Detection rate of the medial axis by the proposed method in a noisy image of standard deviation equal to 0.3 is 68% higher than prior methods.
A new Hessian-based method for medial axis vessel segmentation was developed and tested. This method produced superior results compared to prior methods. This new method has the potential for many applications of medial-axis enhancement.

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To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D registration techniques, which use X-ray fluoroscopic images and computer-aided design (CAD) model of the knee implant, have attracted attention in recent years. These techniques could provide information regarding the movement of radiopaque femoral and tibial components but could not provide information of radiolucent polyethylene insert, because the insert silhouette on X-ray image did not appear clearly. Therefore, it was difficult to obtain 3D kinemaitcs of polyethylene insert, particularly mobile-bearing insert that move on the tibial component. This study presents a technique and the accuracy for 3D kinematic analysis of mobile-bearing insert in TKA using X-ray fluoroscopy, and finally performs clinical applications. For a D pose estimation technique of the mobile-bearing insert in TKA using X-ray fluoroscopy, tantalum beads and CAD model with its beads are utilized, and the 3D pose of the insert model is estimated using a feature-based 2D/3D registration technique. In order to validate the accuracy of the present technique, experiments including computer simulation test were performed. The results showed the pose estimation accuracy was sufficient for analyzing mobile-bearing TKA kinematics (the RMS error: about 1.0 mm, 1.0 degree). In the clinical applications, seven patients with mobile-bearing TKA in deep knee bending motion were studied and analyzed. Consequently, present technique enables us to better understand mobile-bearing TKA kinematics, and this type of evaluation was thought to be helpful for improving implant design and optimizing TKA surgical techniques.

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A computational framework is presented, based on statistical shape modelling, for construction of race-specific organ models for internal radionuclide dosimetry and other nuclear-medicine applications. This approach was applied to the construction of a Japanese liver phantom, using the liver of the digital Zubal phantom as the template and 35 liver computed tomography (CT) scans of male Japanese individuals as a training set. The first step was the automated object-space registration (to align all the liver surfaces in one orientation), using a coherent-point-drift maximum-likelihood alignment algorithm, of each CT scan-derived manually contoured liver surface and the template Zubal liver phantom. Six landmark points, corresponding to the intersection of the contours of the maximum-area sagittal, transaxial and coronal liver sections were employed to perform the above task. To find correspondence points in livers (i.e. 2000 points for each liver), each liver surface was transformed into a mesh, was mapped for the parameter space of a sphere (parameterisation), yielding spherical harmonics (SPHARMs) shape descriptors. The resulting spherical transforms were then registered by minimising the root-mean-square distance among the SPHARMs coefficients. A mean shape (i.e. liver) and its dispersion (i.e. covariance matrix) were next calculated and analysed by principal components. Leave-one-out-tests using 5-35 principal components (or modes) demonstrated the fidelity of the foregoing statistical analysis. Finally, a voxelisation algorithm and a point-based registration is utilised to convert the SPHARM surfaces into its corresponding voxelised and adjusted the Zubal phantom data, respectively. The proposed technique used to create the race-specific statistical phantom maintains anatomic realism and provides the statistical parameters for application to radionuclide dosimetry.

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Since the view direction of an oblique-viewing endoscope can be changed by rotating the scope cylinder to obtain a larger field of view, the rotation angle of the scope cylinder is one of the parameters in its camera model. In order to perform the augmented reality visualization in the surgery using the oblique-viewing endoscope, tracking of the rotation angle is required to determine the view direction. The optical tracking markers or the rotary-encoder have been used for the rotation tacking in the previous studies. However, the additional components attached to the endoscope can be obstructive during the surgery. We propose the rotation tracking method without any additional components based on detection of the wedge mark in the endoscopie image, which indicates the cylinder angle. In the experiment, the difference of the proposed method from the rotary encoder used as the gold standard was less than 1.5 degrees.

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In conventional open surgery, intraoperative surgical planning was determined by unifying all the human senses, based on a
combination of the surgeon’s experience and intraoperative information, obtained, for example, from direct vision and tactile
feedback. In order to update surgical planning during surgery, the surgeon has needed extensive imagination to combine the
intraoperative anatomical information with preoperative 2-D images or with knowledge based on surgical experience. With the
significant advantage of minimal invasiveness for the patients, emerging surgical techniques, such as endoscopic surgery,
percutaneous intervention, or the extracorporeal approach, have increasingly become alternative therapeutics to the conventional
open approach. However, these have distanced the surgeon from the real surgical field, diminishing his understanding through
limited access. The emerging 3-D endoscope, the flexible endoscope, and robot arms have successfully compensated for some
of the disadvantages during laparoscopic surgery. However, in the modern era of minimally invasive urology, the search for
new technology has continued, to compensate for the diminished human understanding, to decrease the learning curve, or even
to make the new approach superior to the conventional approach. Emerging computer-aided digital imaging technology provides
a powerful new opportunity to obtain real-time 3-D visualization of the surgical fields, even beyond the surgical view, for
the surgeon to have an idea of how to optimize the surgical approach and how to decrease surgical morbidity.